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/** Authors: Ethan Rublee, Vincent Rabaud, Gary Bradski */

#include <opencv2/opencv.hpp>
////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

#include "ORB.h"
namespace markerAR
{
  
  const float HARRIS_K = 0.04f;
  
  /**
   * Function that computes the Harris responses in a
   * blockSize x blockSize patch at given points in an image
   */
  static void
  HarrisResponses(const cv::Mat& img, std::vector<cv::KeyPoint>& pts, int blockSize, float harris_k)
  {
    CV_Assert(img.type() == CV_8UC1 && blockSize*blockSize <= 2048);
    
    size_t ptidx, ptsize = pts.size();
    
    const uchar* ptr00 = img.ptr<uchar>();
    int step = (int)(img.step / img.elemSize1());
    int r = blockSize / 2;
    
    float scale = (1 << 2) * blockSize * 255.0f;
    scale = 1.0f / scale;
    float scale_sq_sq = scale * scale * scale * scale;
    
    cv::AutoBuffer<int> ofsbuf(blockSize*blockSize);
    int* ofs = ofsbuf;
    for (int i = 0; i < blockSize; i++)
      for (int j = 0; j < blockSize; j++)
        ofs[i*blockSize + j] = (int)(i*step + j);
    
    for (ptidx = 0; ptidx < ptsize; ptidx++)
    {
      int x0 = cvRound(pts[ptidx].pt.x - r);
      int y0 = cvRound(pts[ptidx].pt.y - r);
      
      const uchar* ptr0 = ptr00 + y0*step + x0;
      int a = 0, b = 0, c = 0;
      
      for (int k = 0; k < blockSize*blockSize; k++)
      {
        const uchar* ptr = ptr0 + ofs[k];
        int Ix = (ptr[1] - ptr[-1]) * 2 + (ptr[-step + 1] - ptr[-step - 1]) + (ptr[step + 1] - ptr[step - 1]);
        int Iy = (ptr[step] - ptr[-step]) * 2 + (ptr[step - 1] - ptr[-step - 1]) + (ptr[step + 1] - ptr[-step + 1]);
        a += Ix*Ix;
        b += Iy*Iy;
        c += Ix*Iy;
      }
      pts[ptidx].response = ((float)a * b - (float)c * c -
                             harris_k * ((float)a + b) * ((float)a + b))*scale_sq_sq;
    }
  }
  
  ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
  static float IC_Angle(const cv::Mat& image, const int half_k, cv::Point2f pt,
                        const std::vector<int> & u_max)
  {
    int m_01 = 0, m_10 = 0;
    
    const uchar* center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));
    
    // Treat the center line differently, v=0
    for (int u = -half_k; u <= half_k; ++u)
      m_10 += u * center[u];
    
    // Go line by line in the circular patch
    int step = (int)image.step1();
    for (int v = 1; v <= half_k; ++v)
    {
      // Proceed over the two lines
      int v_sum = 0;
      int d = u_max[v];
      for (int u = -d; u <= d; ++u)
      {
        int val_plus = center[u + v*step], val_minus = center[u - v*step];
        v_sum += (val_plus - val_minus);
        m_10 += u * (val_plus + val_minus);
      }
      m_01 += v * v_sum;
    }
    
    return cv::fastAtan2((float)m_01, (float)m_10);
  }
  
  ////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
  
  static void computeOrbDescriptor(const cv::KeyPoint& kpt,
                                   const cv::Mat& img, const cv::Point* pattern,
                                   uchar* desc, int dsize, int WTA_K)
  {
    float angle = kpt.angle;
    //angle = cvFloor(angle/12)*12.f;
    angle *= (float)(CV_PI / 180.f);
    float a = (float)cos(angle), b = (float)sin(angle);
    
    const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    int step = (int)img.step;
    
    float x, y;
    int ix, iy;
#if 1
#define GET_VALUE(idx) \
(x = pattern[idx].x*a - pattern[idx].y*b, \
y = pattern[idx].x*b + pattern[idx].y*a, \
ix = cvRound(x), \
iy = cvRound(y), \
*(center + iy*step + ix) )
#else
#define GET_VALUE(idx) \
(x = pattern[idx].x*a - pattern[idx].y*b, \
y = pattern[idx].x*b + pattern[idx].y*a, \
ix = cvFloor(x), iy = cvFloor(y), \
x -= ix, y -= iy, \
cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
#endif
    
    if (WTA_K == 2)
    {
      for (int i = 0; i < dsize; ++i, pattern += 16)
      {
        int t0, t1, val;
        t0 = GET_VALUE(0); t1 = GET_VALUE(1);
        val = t0 < t1;
        t0 = GET_VALUE(2); t1 = GET_VALUE(3);
        val |= (t0 < t1) << 1;
        t0 = GET_VALUE(4); t1 = GET_VALUE(5);
        val |= (t0 < t1) << 2;
        t0 = GET_VALUE(6); t1 = GET_VALUE(7);
        val |= (t0 < t1) << 3;
        t0 = GET_VALUE(8); t1 = GET_VALUE(9);
        val |= (t0 < t1) << 4;
        t0 = GET_VALUE(10); t1 = GET_VALUE(11);
        val |= (t0 < t1) << 5;
        t0 = GET_VALUE(12); t1 = GET_VALUE(13);
        val |= (t0 < t1) << 6;
        t0 = GET_VALUE(14); t1 = GET_VALUE(15);
        val |= (t0 < t1) << 7;
        
        desc[i] = (uchar)val;
      }
    }
    else if (WTA_K == 3)
    {
      for (int i = 0; i < dsize; ++i, pattern += 12)
      {
        int t0, t1, t2, val;
        t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
        val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
        
        t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
        val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
        
        t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
        val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
        
        t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
        val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
        
        desc[i] = (uchar)val;
      }
    }
    else if (WTA_K == 4)
    {
      for (int i = 0; i < dsize; ++i, pattern += 16)
      {
        int t0, t1, t2, t3, u, v, k, val;
        t0 = GET_VALUE(0); t1 = GET_VALUE(1);
        t2 = GET_VALUE(2); t3 = GET_VALUE(3);
        u = 0, v = 2;
        if (t1 > t0) t0 = t1, u = 1;
        if (t3 > t2) t2 = t3, v = 3;
        k = t0 > t2 ? u : v;
        val = k;
        
        t0 = GET_VALUE(4); t1 = GET_VALUE(5);
        t2 = GET_VALUE(6); t3 = GET_VALUE(7);
        u = 0, v = 2;
        if (t1 > t0) t0 = t1, u = 1;
        if (t3 > t2) t2 = t3, v = 3;
        k = t0 > t2 ? u : v;
        val |= k << 2;
        
        t0 = GET_VALUE(8); t1 = GET_VALUE(9);
        t2 = GET_VALUE(10); t3 = GET_VALUE(11);
        u = 0, v = 2;
        if (t1 > t0) t0 = t1, u = 1;
        if (t3 > t2) t2 = t3, v = 3;
        k = t0 > t2 ? u : v;
        val |= k << 4;
        
        t0 = GET_VALUE(12); t1 = GET_VALUE(13);
        t2 = GET_VALUE(14); t3 = GET_VALUE(15);
        u = 0, v = 2;
        if (t1 > t0) t0 = t1, u = 1;
        if (t3 > t2) t2 = t3, v = 3;
        k = t0 > t2 ? u : v;
        val |= k << 6;
        
        desc[i] = (uchar)val;
      }
    }
    else
      CV_Error(CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4.");
    
#undef GET_VALUE
  }
  
  
  static void initializeOrbPattern(const cv::Point* pattern0, std::vector<cv::Point>& pattern, int ntuples, int tupleSize, int poolSize)
  {
    cv::RNG rng(0x12345678);
    int i, k, k1;
    pattern.resize(ntuples*tupleSize);
    
    for (i = 0; i < ntuples; i++)
    {
      for (k = 0; k < tupleSize; k++)
      {
        for (;;)
        {
          int idx = rng.uniform(0, poolSize);
          cv::Point pt = pattern0[idx];
          for (k1 = 0; k1 < k; k1++)
            if (pattern[tupleSize*i + k1] == pt)
              break;
          if (k1 == k)
          {
            pattern[tupleSize*i + k] = pt;
            break;
          }
        }
      }
    }
  }
  
  static int bit_pattern_31_[256 * 4] =
  {
    8, -3, 9, 5/*mean (0), correlation (0)*/,
    4, 2, 7, -12/*mean (1.12461e-05), correlation (0.0437584)*/,
    -11, 9, -8, 2/*mean (3.37382e-05), correlation (0.0617409)*/,
    7, -12, 12, -13/*mean (5.62303e-05), correlation (0.0636977)*/,
    2, -13, 2, 12/*mean (0.000134953), correlation (0.085099)*/,
    1, -7, 1, 6/*mean (0.000528565), correlation (0.0857175)*/,
    -2, -10, -2, -4/*mean (0.0188821), correlation (0.0985774)*/,
    -13, -13, -11, -8/*mean (0.0363135), correlation (0.0899616)*/,
    -13, -3, -12, -9/*mean (0.121806), correlation (0.099849)*/,
    10, 4, 11, 9/*mean (0.122065), correlation (0.093285)*/,
    -13, -8, -8, -9/*mean (0.162787), correlation (0.0942748)*/,
    -11, 7, -9, 12/*mean (0.21561), correlation (0.0974438)*/,
    7, 7, 12, 6/*mean (0.160583), correlation (0.130064)*/,
    -4, -5, -3, 0/*mean (0.228171), correlation (0.132998)*/,
    -13, 2, -12, -3/*mean (0.00997526), correlation (0.145926)*/,
    -9, 0, -7, 5/*mean (0.198234), correlation (0.143636)*/,
    12, -6, 12, -1/*mean (0.0676226), correlation (0.16689)*/,
    -3, 6, -2, 12/*mean (0.166847), correlation (0.171682)*/,
    -6, -13, -4, -8/*mean (0.101215), correlation (0.179716)*/,
    11, -13, 12, -8/*mean (0.200641), correlation (0.192279)*/,
    4, 7, 5, 1/*mean (0.205106), correlation (0.186848)*/,
    5, -3, 10, -3/*mean (0.234908), correlation (0.192319)*/,
    3, -7, 6, 12/*mean (0.0709964), correlation (0.210872)*/,
    -8, -7, -6, -2/*mean (0.0939834), correlation (0.212589)*/,
    -2, 11, -1, -10/*mean (0.127778), correlation (0.20866)*/,
    -13, 12, -8, 10/*mean (0.14783), correlation (0.206356)*/,
    -7, 3, -5, -3/*mean (0.182141), correlation (0.198942)*/,
    -4, 2, -3, 7/*mean (0.188237), correlation (0.21384)*/,
    -10, -12, -6, 11/*mean (0.14865), correlation (0.23571)*/,
    5, -12, 6, -7/*mean (0.222312), correlation (0.23324)*/,
    5, -6, 7, -1/*mean (0.229082), correlation (0.23389)*/,
    1, 0, 4, -5/*mean (0.241577), correlation (0.215286)*/,
    9, 11, 11, -13/*mean (0.00338507), correlation (0.251373)*/,
    4, 7, 4, 12/*mean (0.131005), correlation (0.257622)*/,
    2, -1, 4, 4/*mean (0.152755), correlation (0.255205)*/,
    -4, -12, -2, 7/*mean (0.182771), correlation (0.244867)*/,
    -8, -5, -7, -10/*mean (0.186898), correlation (0.23901)*/,
    4, 11, 9, 12/*mean (0.226226), correlation (0.258255)*/,
    0, -8, 1, -13/*mean (0.0897886), correlation (0.274827)*/,
    -13, -2, -8, 2/*mean (0.148774), correlation (0.28065)*/,
    -3, -2, -2, 3/*mean (0.153048), correlation (0.283063)*/,
    -6, 9, -4, -9/*mean (0.169523), correlation (0.278248)*/,
    8, 12, 10, 7/*mean (0.225337), correlation (0.282851)*/,
    0, 9, 1, 3/*mean (0.226687), correlation (0.278734)*/,
    7, -5, 11, -10/*mean (0.00693882), correlation (0.305161)*/,
    -13, -6, -11, 0/*mean (0.0227283), correlation (0.300181)*/,
    10, 7, 12, 1/*mean (0.125517), correlation (0.31089)*/,
    -6, -3, -6, 12/*mean (0.131748), correlation (0.312779)*/,
    10, -9, 12, -4/*mean (0.144827), correlation (0.292797)*/,
    -13, 8, -8, -12/*mean (0.149202), correlation (0.308918)*/,
    -13, 0, -8, -4/*mean (0.160909), correlation (0.310013)*/,
    3, 3, 7, 8/*mean (0.177755), correlation (0.309394)*/,
    5, 7, 10, -7/*mean (0.212337), correlation (0.310315)*/,
    -1, 7, 1, -12/*mean (0.214429), correlation (0.311933)*/,
    3, -10, 5, 6/*mean (0.235807), correlation (0.313104)*/,
    2, -4, 3, -10/*mean (0.00494827), correlation (0.344948)*/,
    -13, 0, -13, 5/*mean (0.0549145), correlation (0.344675)*/,
    -13, -7, -12, 12/*mean (0.103385), correlation (0.342715)*/,
    -13, 3, -11, 8/*mean (0.134222), correlation (0.322922)*/,
    -7, 12, -4, 7/*mean (0.153284), correlation (0.337061)*/,
    6, -10, 12, 8/*mean (0.154881), correlation (0.329257)*/,
    -9, -1, -7, -6/*mean (0.200967), correlation (0.33312)*/,
    -2, -5, 0, 12/*mean (0.201518), correlation (0.340635)*/,
    -12, 5, -7, 5/*mean (0.207805), correlation (0.335631)*/,
    3, -10, 8, -13/*mean (0.224438), correlation (0.34504)*/,
    -7, -7, -4, 5/*mean (0.239361), correlation (0.338053)*/,
    -3, -2, -1, -7/*mean (0.240744), correlation (0.344322)*/,
    2, 9, 5, -11/*mean (0.242949), correlation (0.34145)*/,
    -11, -13, -5, -13/*mean (0.244028), correlation (0.336861)*/,
    -1, 6, 0, -1/*mean (0.247571), correlation (0.343684)*/,
    5, -3, 5, 2/*mean (0.000697256), correlation (0.357265)*/,
    -4, -13, -4, 12/*mean (0.00213675), correlation (0.373827)*/,
    -9, -6, -9, 6/*mean (0.0126856), correlation (0.373938)*/,
    -12, -10, -8, -4/*mean (0.0152497), correlation (0.364237)*/,
    10, 2, 12, -3/*mean (0.0299933), correlation (0.345292)*/,
    7, 12, 12, 12/*mean (0.0307242), correlation (0.366299)*/,
    -7, -13, -6, 5/*mean (0.0534975), correlation (0.368357)*/,
    -4, 9, -3, 4/*mean (0.099865), correlation (0.372276)*/,
    7, -1, 12, 2/*mean (0.117083), correlation (0.364529)*/,
    -7, 6, -5, 1/*mean (0.126125), correlation (0.369606)*/,
    -13, 11, -12, 5/*mean (0.130364), correlation (0.358502)*/,
    -3, 7, -2, -6/*mean (0.131691), correlation (0.375531)*/,
    7, -8, 12, -7/*mean (0.160166), correlation (0.379508)*/,
    -13, -7, -11, -12/*mean (0.167848), correlation (0.353343)*/,
    1, -3, 12, 12/*mean (0.183378), correlation (0.371916)*/,
    2, -6, 3, 0/*mean (0.228711), correlation (0.371761)*/,
    -4, 3, -2, -13/*mean (0.247211), correlation (0.364063)*/,
    -1, -13, 1, 9/*mean (0.249325), correlation (0.378139)*/,
    7, 1, 8, -6/*mean (0.000652272), correlation (0.411682)*/,
    1, -1, 3, 12/*mean (0.00248538), correlation (0.392988)*/,
    9, 1, 12, 6/*mean (0.0206815), correlation (0.386106)*/,
    -1, -9, -1, 3/*mean (0.0364485), correlation (0.410752)*/,
    -13, -13, -10, 5/*mean (0.0376068), correlation (0.398374)*/,
    7, 7, 10, 12/*mean (0.0424202), correlation (0.405663)*/,
    12, -5, 12, 9/*mean (0.0942645), correlation (0.410422)*/,
    6, 3, 7, 11/*mean (0.1074), correlation (0.413224)*/,
    5, -13, 6, 10/*mean (0.109256), correlation (0.408646)*/,
    2, -12, 2, 3/*mean (0.131691), correlation (0.416076)*/,
    3, 8, 4, -6/*mean (0.165081), correlation (0.417569)*/,
    2, 6, 12, -13/*mean (0.171874), correlation (0.408471)*/,
    9, -12, 10, 3/*mean (0.175146), correlation (0.41296)*/,
    -8, 4, -7, 9/*mean (0.183682), correlation (0.402956)*/,
    -11, 12, -4, -6/*mean (0.184672), correlation (0.416125)*/,
    1, 12, 2, -8/*mean (0.191487), correlation (0.386696)*/,
    6, -9, 7, -4/*mean (0.192668), correlation (0.394771)*/,
    2, 3, 3, -2/*mean (0.200157), correlation (0.408303)*/,
    6, 3, 11, 0/*mean (0.204588), correlation (0.411762)*/,
    3, -3, 8, -8/*mean (0.205904), correlation (0.416294)*/,
    7, 8, 9, 3/*mean (0.213237), correlation (0.409306)*/,
    -11, -5, -6, -4/*mean (0.243444), correlation (0.395069)*/,
    -10, 11, -5, 10/*mean (0.247672), correlation (0.413392)*/,
    -5, -8, -3, 12/*mean (0.24774), correlation (0.411416)*/,
    -10, 5, -9, 0/*mean (0.00213675), correlation (0.454003)*/,
    8, -1, 12, -6/*mean (0.0293635), correlation (0.455368)*/,
    4, -6, 6, -11/*mean (0.0404971), correlation (0.457393)*/,
    -10, 12, -8, 7/*mean (0.0481107), correlation (0.448364)*/,
    4, -2, 6, 7/*mean (0.050641), correlation (0.455019)*/,
    -2, 0, -2, 12/*mean (0.0525978), correlation (0.44338)*/,
    -5, -8, -5, 2/*mean (0.0629667), correlation (0.457096)*/,
    7, -6, 10, 12/*mean (0.0653846), correlation (0.445623)*/,
    -9, -13, -8, -8/*mean (0.0858749), correlation (0.449789)*/,
    -5, -13, -5, -2/*mean (0.122402), correlation (0.450201)*/,
    8, -8, 9, -13/*mean (0.125416), correlation (0.453224)*/,
    -9, -11, -9, 0/*mean (0.130128), correlation (0.458724)*/,
    1, -8, 1, -2/*mean (0.132467), correlation (0.440133)*/,
    7, -4, 9, 1/*mean (0.132692), correlation (0.454)*/,
    -2, 1, -1, -4/*mean (0.135695), correlation (0.455739)*/,
    11, -6, 12, -11/*mean (0.142904), correlation (0.446114)*/,
    -12, -9, -6, 4/*mean (0.146165), correlation (0.451473)*/,
    3, 7, 7, 12/*mean (0.147627), correlation (0.456643)*/,
    5, 5, 10, 8/*mean (0.152901), correlation (0.455036)*/,
    0, -4, 2, 8/*mean (0.167083), correlation (0.459315)*/,
    -9, 12, -5, -13/*mean (0.173234), correlation (0.454706)*/,
    0, 7, 2, 12/*mean (0.18312), correlation (0.433855)*/,
    -1, 2, 1, 7/*mean (0.185504), correlation (0.443838)*/,
    5, 11, 7, -9/*mean (0.185706), correlation (0.451123)*/,
    3, 5, 6, -8/*mean (0.188968), correlation (0.455808)*/,
    -13, -4, -8, 9/*mean (0.191667), correlation (0.459128)*/,
    -5, 9, -3, -3/*mean (0.193196), correlation (0.458364)*/,
    -4, -7, -3, -12/*mean (0.196536), correlation (0.455782)*/,
    6, 5, 8, 0/*mean (0.1972), correlation (0.450481)*/,
    -7, 6, -6, 12/*mean (0.199438), correlation (0.458156)*/,
    -13, 6, -5, -2/*mean (0.211224), correlation (0.449548)*/,
    1, -10, 3, 10/*mean (0.211718), correlation (0.440606)*/,
    4, 1, 8, -4/*mean (0.213034), correlation (0.443177)*/,
    -2, -2, 2, -13/*mean (0.234334), correlation (0.455304)*/,
    2, -12, 12, 12/*mean (0.235684), correlation (0.443436)*/,
    -2, -13, 0, -6/*mean (0.237674), correlation (0.452525)*/,
    4, 1, 9, 3/*mean (0.23962), correlation (0.444824)*/,
    -6, -10, -3, -5/*mean (0.248459), correlation (0.439621)*/,
    -3, -13, -1, 1/*mean (0.249505), correlation (0.456666)*/,
    7, 5, 12, -11/*mean (0.00119208), correlation (0.495466)*/,
    4, -2, 5, -7/*mean (0.00372245), correlation (0.484214)*/,
    -13, 9, -9, -5/*mean (0.00741116), correlation (0.499854)*/,
    7, 1, 8, 6/*mean (0.0208952), correlation (0.499773)*/,
    7, -8, 7, 6/*mean (0.0220085), correlation (0.501609)*/,
    -7, -4, -7, 1/*mean (0.0233806), correlation (0.496568)*/,
    -8, 11, -7, -8/*mean (0.0236505), correlation (0.489719)*/,
    -13, 6, -12, -8/*mean (0.0268781), correlation (0.503487)*/,
    2, 4, 3, 9/*mean (0.0323324), correlation (0.501938)*/,
    10, -5, 12, 3/*mean (0.0399235), correlation (0.494029)*/,
    -6, -5, -6, 7/*mean (0.0420153), correlation (0.486579)*/,
    8, -3, 9, -8/*mean (0.0548021), correlation (0.484237)*/,
    2, -12, 2, 8/*mean (0.0616622), correlation (0.496642)*/,
    -11, -2, -10, 3/*mean (0.0627755), correlation (0.498563)*/,
    -12, -13, -7, -9/*mean (0.0829622), correlation (0.495491)*/,
    -11, 0, -10, -5/*mean (0.0843342), correlation (0.487146)*/,
    5, -3, 11, 8/*mean (0.0929937), correlation (0.502315)*/,
    -2, -13, -1, 12/*mean (0.113327), correlation (0.48941)*/,
    -1, -8, 0, 9/*mean (0.132119), correlation (0.467268)*/,
    -13, -11, -12, -5/*mean (0.136269), correlation (0.498771)*/,
    -10, -2, -10, 11/*mean (0.142173), correlation (0.498714)*/,
    -3, 9, -2, -13/*mean (0.144141), correlation (0.491973)*/,
    2, -3, 3, 2/*mean (0.14892), correlation (0.500782)*/,
    -9, -13, -4, 0/*mean (0.150371), correlation (0.498211)*/,
    -4, 6, -3, -10/*mean (0.152159), correlation (0.495547)*/,
    -4, 12, -2, -7/*mean (0.156152), correlation (0.496925)*/,
    -6, -11, -4, 9/*mean (0.15749), correlation (0.499222)*/,
    6, -3, 6, 11/*mean (0.159211), correlation (0.503821)*/,
    -13, 11, -5, 5/*mean (0.162427), correlation (0.501907)*/,
    11, 11, 12, 6/*mean (0.16652), correlation (0.497632)*/,
    7, -5, 12, -2/*mean (0.169141), correlation (0.484474)*/,
    -1, 12, 0, 7/*mean (0.169456), correlation (0.495339)*/,
    -4, -8, -3, -2/*mean (0.171457), correlation (0.487251)*/,
    -7, 1, -6, 7/*mean (0.175), correlation (0.500024)*/,
    -13, -12, -8, -13/*mean (0.175866), correlation (0.497523)*/,
    -7, -2, -6, -8/*mean (0.178273), correlation (0.501854)*/,
    -8, 5, -6, -9/*mean (0.181107), correlation (0.494888)*/,
    -5, -1, -4, 5/*mean (0.190227), correlation (0.482557)*/,
    -13, 7, -8, 10/*mean (0.196739), correlation (0.496503)*/,
    1, 5, 5, -13/*mean (0.19973), correlation (0.499759)*/,
    1, 0, 10, -13/*mean (0.204465), correlation (0.49873)*/,
    9, 12, 10, -1/*mean (0.209334), correlation (0.49063)*/,
    5, -8, 10, -9/*mean (0.211134), correlation (0.503011)*/,
    -1, 11, 1, -13/*mean (0.212), correlation (0.499414)*/,
    -9, -3, -6, 2/*mean (0.212168), correlation (0.480739)*/,
    -1, -10, 1, 12/*mean (0.212731), correlation (0.502523)*/,
    -13, 1, -8, -10/*mean (0.21327), correlation (0.489786)*/,
    8, -11, 10, -6/*mean (0.214159), correlation (0.488246)*/,
    2, -13, 3, -6/*mean (0.216993), correlation (0.50287)*/,
    7, -13, 12, -9/*mean (0.223639), correlation (0.470502)*/,
    -10, -10, -5, -7/*mean (0.224089), correlation (0.500852)*/,
    -10, -8, -8, -13/*mean (0.228666), correlation (0.502629)*/,
    4, -6, 8, 5/*mean (0.22906), correlation (0.498305)*/,
    3, 12, 8, -13/*mean (0.233378), correlation (0.503825)*/,
    -4, 2, -3, -3/*mean (0.234323), correlation (0.476692)*/,
    5, -13, 10, -12/*mean (0.236392), correlation (0.475462)*/,
    4, -13, 5, -1/*mean (0.236842), correlation (0.504132)*/,
    -9, 9, -4, 3/*mean (0.236977), correlation (0.497739)*/,
    0, 3, 3, -9/*mean (0.24314), correlation (0.499398)*/,
    -12, 1, -6, 1/*mean (0.243297), correlation (0.489447)*/,
    3, 2, 4, -8/*mean (0.00155196), correlation (0.553496)*/,
    -10, -10, -10, 9/*mean (0.00239541), correlation (0.54297)*/,
    8, -13, 12, 12/*mean (0.0034413), correlation (0.544361)*/,
    -8, -12, -6, -5/*mean (0.003565), correlation (0.551225)*/,
    2, 2, 3, 7/*mean (0.00835583), correlation (0.55285)*/,
    10, 6, 11, -8/*mean (0.00885065), correlation (0.540913)*/,
    6, 8, 8, -12/*mean (0.0101552), correlation (0.551085)*/,
    -7, 10, -6, 5/*mean (0.0102227), correlation (0.533635)*/,
    -3, -9, -3, 9/*mean (0.0110211), correlation (0.543121)*/,
    -1, -13, -1, 5/*mean (0.0113473), correlation (0.550173)*/,
    -3, -7, -3, 4/*mean (0.0140913), correlation (0.554774)*/,
    -8, -2, -8, 3/*mean (0.017049), correlation (0.55461)*/,
    4, 2, 12, 12/*mean (0.01778), correlation (0.546921)*/,
    2, -5, 3, 11/*mean (0.0224022), correlation (0.549667)*/,
    6, -9, 11, -13/*mean (0.029161), correlation (0.546295)*/,
    3, -1, 7, 12/*mean (0.0303081), correlation (0.548599)*/,
    11, -1, 12, 4/*mean (0.0355151), correlation (0.523943)*/,
    -3, 0, -3, 6/*mean (0.0417904), correlation (0.543395)*/,
    4, -11, 4, 12/*mean (0.0487292), correlation (0.542818)*/,
    2, -4, 2, 1/*mean (0.0575124), correlation (0.554888)*/,
    -10, -6, -8, 1/*mean (0.0594242), correlation (0.544026)*/,
    -13, 7, -11, 1/*mean (0.0597391), correlation (0.550524)*/,
    -13, 12, -11, -13/*mean (0.0608974), correlation (0.55383)*/,
    6, 0, 11, -13/*mean (0.065126), correlation (0.552006)*/,
    0, -1, 1, 4/*mean (0.074224), correlation (0.546372)*/,
    -13, 3, -9, -2/*mean (0.0808592), correlation (0.554875)*/,
    -9, 8, -6, -3/*mean (0.0883378), correlation (0.551178)*/,
    -13, -6, -8, -2/*mean (0.0901035), correlation (0.548446)*/,
    5, -9, 8, 10/*mean (0.0949843), correlation (0.554694)*/,
    2, 7, 3, -9/*mean (0.0994152), correlation (0.550979)*/,
    -1, -6, -1, -1/*mean (0.10045), correlation (0.552714)*/,
    9, 5, 11, -2/*mean (0.100686), correlation (0.552594)*/,
    11, -3, 12, -8/*mean (0.101091), correlation (0.532394)*/,
    3, 0, 3, 5/*mean (0.101147), correlation (0.525576)*/,
    -1, 4, 0, 10/*mean (0.105263), correlation (0.531498)*/,
    3, -6, 4, 5/*mean (0.110785), correlation (0.540491)*/,
    -13, 0, -10, 5/*mean (0.112798), correlation (0.536582)*/,
    5, 8, 12, 11/*mean (0.114181), correlation (0.555793)*/,
    8, 9, 9, -6/*mean (0.117431), correlation (0.553763)*/,
    7, -4, 8, -12/*mean (0.118522), correlation (0.553452)*/,
    -10, 4, -10, 9/*mean (0.12094), correlation (0.554785)*/,
    7, 3, 12, 4/*mean (0.122582), correlation (0.555825)*/,
    9, -7, 10, -2/*mean (0.124978), correlation (0.549846)*/,
    7, 0, 12, -2/*mean (0.127002), correlation (0.537452)*/,
    -1, -6, 0, -11/*mean (0.127148), correlation (0.547401)*/
  };
  
  
  static void makeRandomPattern(int patchSize, cv::Point* pattern, int npoints)
  {
    cv::RNG rng(0x34985739); // we always start with a fixed seed,
                             // to make patterns the same on each run
    for (int i = 0; i < npoints; i++)
    {
      pattern[i].x = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
      pattern[i].y = rng.uniform(-patchSize / 2, patchSize / 2 + 1);
    }
  }
  
  
  static inline float getScale(int level, int firstLevel, double scaleFactor)
  {
    return (float)std::pow(scaleFactor, (double)(level - firstLevel));
  }
  
  ///** Constructor
  //* @param detector_params parameters to use
  //*/
  //ORB::ORB(int _nfeatures, float _scaleFactor, int _nlevels, int _edgeThreshold,
  //	int _firstLevel, int _WTA_K, int _scoreType, int _patchSize) :
  //	nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels),
  //	edgeThreshold(_edgeThreshold), firstLevel(_firstLevel), WTA_K(_WTA_K),
  //	scoreType(_scoreType), patchSize(_patchSize)
  //{}
  
  
  int ORB::descriptorSize() const
  {
    return kBytes;
  }
  
  int ORB::descriptorType() const
  {
    return CV_8U;
  }
  
  /** Compute the ORB features and descriptors on an image
   * @param img the image to compute the features and descriptors on
   * @param mask the mask to apply
   * @param keypoints the resulting keypoints
   */
  void ORB::operator()(cv::InputArray image, cv::InputArray mask, std::vector<cv::KeyPoint>& keypoints)
  {
    (*this)(image, mask, keypoints, cv::noArray(), false);
  }
  
  
  /** Compute the ORB keypoint orientations
   * @param image the image to compute the features and descriptors on
   * @param integral_image the integral image of the iamge (can be empty, but the computation will be slower)
   * @param scale the scale at which we compute the orientation
   * @param keypoints the resulting keypoints
   */
  static void computeOrientation(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints,
                                 int halfPatchSize, const std::vector<int>& umax)
  {
    // Process each keypoint
    for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
         keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
    {
      keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
    }
  }
  
  
  /** Compute the ORB keypoints on an image
   * @param image_pyramid the image pyramid to compute the features and descriptors on
   * @param mask_pyramid the masks to apply at every level
   * @param keypoints the resulting keypoints, clustered per level
   */
  void ORB::computeKeyPoints(const std::vector<cv::Mat>& imagePyramid,
                             const std::vector<cv::Mat>& maskPyramid,
                             std::vector<std::vector<cv::KeyPoint> >& allKeypoints,
                             int nfeatures, int firstLevel, double scaleFactor,
                             int edgeThreshold, int patchSize, int scoreType)
  {
    int nlevels = (int)imagePyramid.size();
    std::vector<int> nfeaturesPerLevel(nlevels);
    
    // fill the extractors and descriptors for the corresponding scales
    float factor = (float)(1.0 / scaleFactor);
    float ndesiredFeaturesPerScale = nfeatures*(1 - factor) / (1 - (float)pow((double)factor, (double)nlevels));
    
    int sumFeatures = 0;
    for (int level = 0; level < nlevels - 1; level++)
    {
      nfeaturesPerLevel[level] = cvRound(ndesiredFeaturesPerScale);
      sumFeatures += nfeaturesPerLevel[level];
      ndesiredFeaturesPerScale *= factor;
    }
    nfeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);
    
    // Make sure we forget about what is too close to the boundary
    //edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);
    
    // pre-compute the end of a row in a circular patch
    int halfPatchSize = patchSize / 2;
    std::vector<int> umax(halfPatchSize + 2);
    
    int v, v0, vmax = cvFloor(halfPatchSize * sqrt(2.f) / 2 + 1);
    int vmin = cvCeil(halfPatchSize * sqrt(2.f) / 2);
    for (v = 0; v <= vmax; ++v)
      umax[v] = cvRound(sqrt((double)halfPatchSize * halfPatchSize - v * v));
    
    // Make sure we are symmetric
    for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
    {
      while (umax[v0] == umax[v0 + 1])
        ++v0;
      umax[v] = v0;
      ++v0;
    }
    
    allKeypoints.resize(nlevels);
    
    for (int level = 0; level < nlevels; ++level)
    {
      int featuresNum = nfeaturesPerLevel[level];
      allKeypoints[level].reserve(featuresNum * 2);
      
      std::vector<cv::KeyPoint> & keypoints = allKeypoints[level];
      
      // Detect FAST features, 20 is a good threshold
      fd.detect(imagePyramid[level], keypoints, maskPyramid[level],featuresNum,edgeThreshold);
      // Remove keypoints very close to the border
//      cv::KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
//      vector<int> idx;
//      if (doAnms)
//        anms->anms(keypoints, featuresNum, idx, imagePyramid[level].size());
      if (scoreType == ORB::HARRIS_SCORE)
      {
        // Keep more points than necessary as FAST does not give amazing corners
        cv::KeyPointsFilter::retainBest(keypoints, 2 * featuresNum);
        
        // Compute the Harris cornerness (better scoring than FAST)
        HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
      }
      
      //cull to the final desired level, using the new Harris scores or the original FAST scores.
////      if (!doAnms)
//        KeyPointsFilter::retainBest(keypoints, featuresNum);
      
      float sf = getScale(level, firstLevel, scaleFactor);
      
      // Set the level of the coordinates
      for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
           keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
      {
        keypoint->octave = level;
        keypoint->size = patchSize*sf;
      }
      
      computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
    }
  }
  
  
  /** Compute the ORB decriptors
   * @param image the image to compute the features and descriptors on
   * @param integral_image the integral image of the image (can be empty, but the computation will be slower)
   * @param level the scale at which we compute the orientation
   * @param keypoints the keypoints to use
   * @param descriptors the resulting descriptors
   */
  static void computeDescriptors(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors,
                                 const std::vector<cv::Point>& pattern, int dsize, int WTA_K)
  {
    //convert to grayscale if more than one color
    CV_Assert(image.type() == CV_8UC1);
    //create the descriptor mat, keypoints.size() rows, BYTES cols
    descriptors = cv::Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
    
    for (size_t i = 0; i < keypoints.size(); i++)
      computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
  }
  
  
  /** Compute the ORB features and descriptors on an image
   * @param img the image to compute the features and descriptors on
   * @param mask the mask to apply
   * @param keypoints the resulting keypoints
   * @param descriptors the resulting descriptors
   * @param do_keypoints if true, the keypoints are computed, otherwise used as an input
   * @param do_descriptors if true, also computes the descriptors
   */
  void ORB::operator()(cv::InputArray _image, cv::InputArray _mask, std::vector<cv::KeyPoint>& _keypoints,
                       cv::OutputArray _descriptors, bool useProvidedKeypoints)
  {
    CV_Assert(patchSize >= 2);
    
    bool do_keypoints = !useProvidedKeypoints;
    bool do_descriptors = _descriptors.needed();
    
    if ((!do_keypoints && !do_descriptors) || _image.empty())
      return;
    
    m.lock();
    //ROI handling
    const int HARRIS_BLOCK_SIZE = 9;
    int halfPatchSize = patchSize / 2;
    int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE / 2)) + 1;
    
    cv::Mat image = _image.getMat(), mask = _mask.getMat();
    if (image.type() != CV_8UC1)
      cvtColor(_image, image, CV_BGR2GRAY);
    
    int levelsNum = this->nlevels;
    
    if (!do_keypoints)
    {
      // if we have pre-computed keypoints, they may use more levels than it is set in parameters
      // !!!TODO!!! implement more correct method, independent from the used keypoint detector.
      // Namely, the detector should provide correct size of each keypoint. Based on the keypoint size
      // and the algorithm used (i.e. BRIEF, running on 31x31 patches) we should compute the approximate
      // scale-factor that we need to apply. Then we should cluster all the computed scale-factors and
      // for each cluster compute the corresponding image.
      //
      // In short, ultimately the descriptor should
      // ignore octave parameter and deal only with the keypoint size.
      levelsNum = 0;
      for (size_t i = 0; i < _keypoints.size(); i++)
        levelsNum = std::max(levelsNum, std::max(_keypoints[i].octave, 0));
      levelsNum++;
    }
    
    // Pre-compute the scale pyramids
    std::vector<cv::Mat> imagePyramid(levelsNum), maskPyramid(levelsNum);
    for (int level = 0; level < levelsNum; ++level)
    {
      float scale = 1 / getScale(level, firstLevel, scaleFactor);
      cv::Size sz(cvRound(image.cols*scale), cvRound(image.rows*scale));
      cv::Size wholeSize(sz.width + border * 2, sz.height + border * 2);
      cv::Mat temp(wholeSize, image.type()), masktemp;
      imagePyramid[level] = temp(cv::Rect(border, border, sz.width, sz.height));
      
      if (!mask.empty())
      {
        masktemp = cv::Mat(wholeSize, mask.type());
        maskPyramid[level] = masktemp(cv::Rect(border, border, sz.width, sz.height));
      }
      
      // Compute the resized image
      if (level != firstLevel)
      {
        if (level < firstLevel)
        {
          resize(image, imagePyramid[level], sz, 0, 0, cv::INTER_LINEAR);
          if (!mask.empty())
            resize(mask, maskPyramid[level], sz, 0, 0, cv::INTER_LINEAR);
        }
        else
        {
          resize(imagePyramid[level - 1], imagePyramid[level], sz, 0, 0, cv::INTER_LINEAR);
          if (!mask.empty())
          {
            resize(maskPyramid[level - 1], maskPyramid[level], sz, 0, 0, cv::INTER_LINEAR);
            //						threshold(maskPyramid[level], maskPyramid[level], 254, 0, cv::THRESH_TOZERO);
          }
        }
        
        copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
                       cv::BORDER_REFLECT_101 + cv::BORDER_ISOLATED);
        if (!mask.empty())
          copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
                         cv::BORDER_CONSTANT + cv::BORDER_ISOLATED);
      }
      else
      {
        copyMakeBorder(image, temp, border, border, border, border,
                       cv::BORDER_REFLECT_101);
        if (!mask.empty())
          copyMakeBorder(mask, masktemp, border, border, border, border,
                         cv::BORDER_CONSTANT + cv::BORDER_ISOLATED);
      }
    }
    
    // Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
    std::vector < std::vector<cv::KeyPoint> > allKeypoints;
    if (do_keypoints)
    {
      // Get keypoints, those will be far enough from the border that no check will be required for the descriptor
      computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
                       nfeatures, firstLevel, scaleFactor,
                       edgeThreshold, patchSize, scoreType);
      
      // make sure we have the right number of keypoints keypoints
      /*vector<KeyPoint> temp;
       
       for (int level = 0; level < n_levels; ++level)
       {
       vector<KeyPoint>& keypoints = all_keypoints[level];
       temp.insert(temp.end(), keypoints.begin(), keypoints.end());
       keypoints.clear();
       }
       
       KeyPoint::retainBest(temp, n_features_);
       
       for (vector<KeyPoint>::iterator keypoint = temp.begin(),
       keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
       all_keypoints[keypoint->octave].push_back(*keypoint);*/
    }
    else
    {
      // Remove keypoints very close to the border
      cv::KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
      
      // Cluster the input keypoints depending on the level they were computed at
      allKeypoints.resize(levelsNum);
      for (std::vector<cv::KeyPoint>::iterator keypoint = _keypoints.begin(),
           keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
        allKeypoints[keypoint->octave].push_back(*keypoint);
      
      int halfPatchSize = patchSize / 2;
      std::vector<int> umax(halfPatchSize + 2);
      
      int v, v0, vmax = cvFloor(halfPatchSize * sqrt(2.f) / 2 + 1);
      int vmin = cvCeil(halfPatchSize * sqrt(2.f) / 2);
      for (v = 0; v <= vmax; ++v)
        umax[v] = cvRound(sqrt((double)halfPatchSize * halfPatchSize - v * v));
      
      // Make sure we are symmetric
      for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
      {
        while (umax[v0] == umax[v0 + 1])
          ++v0;
        umax[v] = v0;
        ++v0;
      }
      
      
      
      // Make sure we rescale the coordinates
      for (int level = 0; level < levelsNum; ++level)
      {
        std::vector<cv::KeyPoint> & keypoints = allKeypoints[level];
        if (level != firstLevel)
        {
          float scale = 1 / getScale(level, firstLevel, scaleFactor);
          for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
               keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
            keypoint->pt *= scale;
        }
        computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
      }
      
    }
    
    cv::Mat descriptors;
    std::vector<cv::Point> pattern;
    
    if (do_descriptors)
    {
      int nkeypoints = 0;
      for (int level = 0; level < levelsNum; ++level)
        nkeypoints += (int)allKeypoints[level].size();
      if (nkeypoints == 0)
        _descriptors.release();
      else
      {
        _descriptors.create(nkeypoints, descriptorSize(), CV_8U);
        descriptors = _descriptors.getMat();
      }
      
      const int npoints = 512;
      cv::Point patternbuf[npoints];
      const cv::Point* pattern0 = (const cv::Point*)bit_pattern_31_;
      
      if (patchSize != 31)
      {
        pattern0 = patternbuf;
        makeRandomPattern(patchSize, patternbuf, npoints);
      }
      
      CV_Assert(WTA_K == 2 || WTA_K == 3 || WTA_K == 4);
      
      if (WTA_K == 2)
        std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
      else
      {
        int ntuples = descriptorSize() * 4;
        initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
      }
    }
    
    _keypoints.clear();
    int offset = 0;
    for (int level = 0; level < levelsNum; ++level)
    {
      // Get the features and compute their orientation
      std::vector<cv::KeyPoint>& keypoints = allKeypoints[level];
      int nkeypoints = (int)keypoints.size();
      
      // Compute the descriptors
      if (do_descriptors)
      {
        cv::Mat desc;
        if (!descriptors.empty())
        {
          desc = descriptors.rowRange(offset, offset + nkeypoints);
        }
        
        offset += nkeypoints;
        // preprocess the resized image
        cv::Mat& workingMat = imagePyramid[level];
        //boxFilter(working_mat, working_mat, working_mat.depth(), Size(5,5), Point(-1,-1), true, BORDER_REFLECT_101);
        cv::GaussianBlur(workingMat, workingMat, cv::Size(7, 7), 2, 2, cv::BORDER_REFLECT_101);
        computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
      }
      
      // Copy to the output data
      if (level != firstLevel)
      {
        float scale = getScale(level, firstLevel, scaleFactor);
        for (std::vector<cv::KeyPoint>::iterator keypoint = keypoints.begin(),
             keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
          keypoint->pt *= scale;
      }
      // And add the keypoints to the output
      _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
    m.unlock();
  }
  
  void ORB::detect(const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask)
  {
    (*this)(image, mask, keypoints,cv::noArray(), false);
  }
  
  void ORB::detectImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, const cv::Mat& mask)
  {
    (*this)(image, mask, keypoints, cv::noArray(), false);
  }
  
  void ORB::compute(const Mat& image,std::vector<KeyPoint>& keypoints, CV_OUT Mat& descriptors)
  {
    if (image.empty() || keypoints.empty())
    {
      descriptors.release();
      return;
    }
    
    KeyPointsFilter::runByImageBorder(keypoints, image.size(), 0);
    KeyPointsFilter::runByKeypointSize(keypoints, std::numeric_limits<float>::epsilon());
    (*this)(image, cv::Mat(), keypoints, descriptors, true);
  }
  
  void ORB::computeImpl(const cv::Mat& image, std::vector<cv::KeyPoint>& keypoints, cv::Mat& descriptors)
  {
    (*this)(image, cv::Mat(), keypoints, descriptors, true);
  }
  
}
